TY - GEN
T1 - Performance Evaluation of Machine Learning Classifiers for Face Recognition
AU - Sudiana, Dodi
AU - Rizkinia, Mia
AU - Alamsyah, Fahri
N1 - Funding Information:
ACKNOWLEDGMENT This research is funded by Universitas Indonesia Publikasi Terindeks Internasional (PUTI) Q2 Research Grant based on contract No. NKB-4326/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
©2021 IEEE
PY - 2021
Y1 - 2021
N2 - The digital world, especially image processing, has been evolving due to the needs of society and the importance of digital-based system security. One of the rapidly progressing technologies is the face recognition system using artificial intelligence. It recognizes a person's face registered in the database for verification purposes. In this study, we evaluate the face recognition systems based on machine learning classifier algorithms and Principal Component Analysis (PCA) for feature extraction. Seven machine learning algorithms were considered, i.e., Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, Naïve Bayes, Multi-Layer Perceptron (MLP), and Convolutional Neural network (CNN). In the CNN scenario, PCA was not used since it has its feature extraction capability. The first six classifiers were set to the most optimal settings. At the same time, CNN used the LeNet-5 architecture trained with a dropout rate of 0.25, 60 epochs, batch size of 20, Adam optimizer, and cross-categorical entropy for the loss function. The input image size was 64×64×1 obtained from the Olivetti faces database. CNN, SVM, and LR achieved the three highest accuracies, i.e., 98.75%, 98.75%, and 97.50%, respectively.
AB - The digital world, especially image processing, has been evolving due to the needs of society and the importance of digital-based system security. One of the rapidly progressing technologies is the face recognition system using artificial intelligence. It recognizes a person's face registered in the database for verification purposes. In this study, we evaluate the face recognition systems based on machine learning classifier algorithms and Principal Component Analysis (PCA) for feature extraction. Seven machine learning algorithms were considered, i.e., Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, Naïve Bayes, Multi-Layer Perceptron (MLP), and Convolutional Neural network (CNN). In the CNN scenario, PCA was not used since it has its feature extraction capability. The first six classifiers were set to the most optimal settings. At the same time, CNN used the LeNet-5 architecture trained with a dropout rate of 0.25, 60 epochs, batch size of 20, Adam optimizer, and cross-categorical entropy for the loss function. The input image size was 64×64×1 obtained from the Olivetti faces database. CNN, SVM, and LR achieved the three highest accuracies, i.e., 98.75%, 98.75%, and 97.50%, respectively.
KW - Convolutional Neural network (CNN)
KW - Face recognition
KW - machine learning
KW - performance evaluation
KW - Principal Component Analysis (PCA)
UR - http://www.scopus.com/inward/record.url?scp=85126980348&partnerID=8YFLogxK
U2 - 10.1109/QIR54354.2021.9716171
DO - 10.1109/QIR54354.2021.9716171
M3 - Conference contribution
AN - SCOPUS:85126980348
T3 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
SP - 71
EP - 75
BT - 17th International Conference on Quality in Research, QIR 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
Y2 - 13 October 2021 through 15 October 2021
ER -